Generative AI with Azure Database for PostgreSQL

Modern applications require intelligent search and generative capabilities directly integrated with their data stores. This course guides you through bringing the power of large language models and vector search to your relational database. You will transition from managing traditional relational data to building AI-ready database architectures. By understanding how to store, query, and utilize high-dimensional vector embeddings, you will unlock advanced capabilities like semantic search and contextual AI generation without leaving your database environment. What you'll learn: 1. Understand the core concepts of generative AI, vector databases, and embeddings inside a relational context. 2. Configure the pgvector extension to store and index high-dimensional vectors in Azure Database for PostgreSQL. 3. Generate vector embeddings using modern language models and load them into your database. 4. Query semantic data efficiently using distance metrics like cosine similarity. 5. Implement retrieval-augmented generation (RAG) patterns to feed database context to LLMs. 6. Apply best practices for scaling, security, and performance in AI-driven database applications. The course starts with foundational definitions of generative AI and vector search before moving into practical configuration of pgvector. You will then explore implementation strategies for embedding generation, semantic querying, and building retrieval-augmented workflows. This course is designed for database administrators, software developers, and data hobbyists who are new to generative AI and want to leverage their existing PostgreSQL knowledge. No prior machine learning experience is required. Start reading today to bridge the gap between relational databases and modern generative AI.